204 Scopus citations

Abstract

This paper presents a method for evaluating multiple feature spaces while tracking, and for adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. We develop an on-line feature selection mechanism based on the two-class variance ratio measure, applied to log likelihood distributions computed with respect to a given feature from samples of object and background pixels. This feature selection mechanism is embedded in a tracking system that adaptively selects the Top-ranked discriminative features for tracking. Examples are presented to illustrate how the method adapts to changing appearances of both tracked object and scene background.

Original languageEnglish (US)
Pages346-352
Number of pages7
DOIs
StatePublished - 2003
EventProceedings: Ninth IEEE International Conference on Computer Vision - Nice, France
Duration: Oct 13 2003Oct 16 2003

Other

OtherProceedings: Ninth IEEE International Conference on Computer Vision
CountryFrance
CityNice
Period10/13/0310/16/03

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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